ArticleNature communications2021
Divide-and-conquer: machine-learning integrates mammalian and viral traits with network features to predict virus-mammal associations.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed.
- AI-Driven Approaches for the Detection, Classification, and Surveillance of Viral Pathogens: Current Advances, Challenges, and Future Directions.Pathogens (Basel, Switzerland) · 2026Review
- Article
- Protein language models enable accurate viral host range prediction.Scientific reports · 2026Article
- A Unified Framework to Prioritize RNA Virus Cross-Species Transmission Risk Across an Expansive Host Landscape.Viruses · 2026Article
- Host-linked virome assembly and turnover predict bacterial community structure in wastewater treatment systems.The ISME journal · 2026Article
- Zoon0PredV: Potential Virus Species Crossover Prediction Using Convolutional Neural Networks and Viral Protein Sequence Patterns.Bioinformatics and biology insights · 2026Article
- Prediction of Percutaneous Coronary Intervention Success in Patients With Moderate to Severe Coronary Artery Calcification Using Machine Learning Based on Coronary Angiography: Prospective Cohort Study.Journal of medical Internet research · 2025Article
- Viral genomic features predict Orthopoxvirus reservoir hosts.Communications biology · 2025Article
- Viral genomic features predictbioRxiv : the preprint server for biology · 2024Article
- Features that matter: Evolutionary signatures can predict viral transmission routes.PLoS pathogens · 2024Article
- Identifying life-history patterns along the fast-slow continuum of mammalian viral carriers.Royal Society open science · 2024Article
- Predictive phage therapy forProceedings of the National Academy of Sciences of the United States of America · 2024Article
- The potential application of artificial intelligence in veterinary clinical practice and biomedical research.Frontiers in veterinary science · 2024Article
- Network embedding unveils the hidden interactions in the mammalian virome.Patterns (New York, N.Y.) · 2023Article
- Interpreting random forest analysis of ecological models to move from prediction to explanation.Scientific reports · 2023Article
- Bioinformatics approaches for unveiling virus-host interactions.Computational and structural biotechnology journal · 2023Review
- Reply to: Machine-learning prediction of hosts of novel coronaviruses requires caution as it may affect wildlife conservation.Nature communications · 2022Article
- New viruses on the rise: a One Health and ecosystem-based perspective on emerging viruses.Future virology · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Our knowledge of viral host ranges remains limited. Completing this picture by identifying unknown hosts of known viruses is an important research aim that can help identify and mitigate zoonotic and animal-disease risks, such as spill-over from animal reservoirs into human populations. To address this knowledge-gap we apply a divide-and-conquer approach which separates viral, mammalian and network features into three unique perspectives, each predicting associations independently to enhance predictive power. Our approach predicts over 20,000 unknown associations between known viruses and susceptible mammalian species, suggesting that current knowledge underestimates the number of associations in wild and semi-domesticated mammals by a factor of 4.3, and the average potential mammalian host-range of viruses by a factor of 3.2. In particular, our results highlight a significant knowledge gap in the wild reservoirs of important zoonotic and domesticated mammals' viruses: specifically, lyssaviruses, bornaviruses and rotaviruses.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.